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22 April 2024
Reseach Article

Artificial Neural Network Comparison on hERG Channel Blockade Detection

by Haibo Liu, Tessa De Korte, Sylvain Bernasconi, Christophe Bleunven, Damiano Lombardi, Muriel Boulakia
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 14
Year of Publication: 2022
Authors: Haibo Liu, Tessa De Korte, Sylvain Bernasconi, Christophe Bleunven, Damiano Lombardi, Muriel Boulakia
10.5120/ijca2022922119

Haibo Liu, Tessa De Korte, Sylvain Bernasconi, Christophe Bleunven, Damiano Lombardi, Muriel Boulakia . Artificial Neural Network Comparison on hERG Channel Blockade Detection. International Journal of Computer Applications. 184, 14 ( May 2022), 1-9. DOI=10.5120/ijca2022922119

@article{ 10.5120/ijca2022922119,
author = { Haibo Liu, Tessa De Korte, Sylvain Bernasconi, Christophe Bleunven, Damiano Lombardi, Muriel Boulakia },
title = { Artificial Neural Network Comparison on hERG Channel Blockade Detection },
journal = { International Journal of Computer Applications },
issue_date = { May 2022 },
volume = { 184 },
number = { 14 },
month = { May },
year = { 2022 },
issn = { 0975-8887 },
pages = { 1-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number14/32389-2022922119/ },
doi = { 10.5120/ijca2022922119 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:21:26.513794+05:30
%A Haibo Liu
%A Tessa De Korte
%A Sylvain Bernasconi
%A Christophe Bleunven
%A Damiano Lombardi
%A Muriel Boulakia
%T Artificial Neural Network Comparison on hERG Channel Blockade Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 14
%P 1-9
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This work will present a comparison of several Artificial Neural Network methods for a classification problem related to cardiac safety assessment. Given the extracellular field potential recorded by means of micro-electrode arrays, the aim is to determine whether a given chemical drug is altering the electrical activity of cardiomyocytes by disrupting the normal behavior of the hERG channels. To do so, this work has considered four different Neural Network methods and compared them in terms of accuracy and computational costs. The conclusion is that, among the tested architectures, the Multilayer Perceptron (MLP) and multivariate 1-dimensional Convolutional Neural Network (1D-CNN) give the most promising results.

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Index Terms

Computer Science
Information Sciences

Keywords

Artificial Neural Networks classification problems cardiac safety assessment safety pharmacology